臺灣博碩士論文加值系統

車牌辨識系統在近幾年無論在學術或商業上被廣泛的研究與發展，車牌辨識系統主要有三個階段：1. 車牌偵測、2. 字元分割、3. 字元辨識。車牌偵測會使用做以下的影像處理：灰階化、二值化、侵蝕膨脹、連通法等，這些影像處理的各個像素點都有關係，因此會非常頻繁的存取影像資料，和大量的數學運算。在FPGA上資料的傳遞只需要儲存在暫存器，不需存回記憶體，而且可以合成大量的運算單元，因此本論文利用FPGA將影像處理管線化，不管是讀取或是儲存記憶體的次數大量減少，加快整體的系統的速度。
車牌辨識系統的車牌定位通常是以灰階加上二值化，再以邊緣偵測或是尋找物件的方法來找尋車牌，但是灰階會將大量的資料捨去，台灣的車牌並非只有白色底，也有綠色、紅色和黃色，所以要是尋找其他顏色底的車牌會非常困難，因此本論文利用HSI(Hue Saturation Intensity)的方法利用色調來做二值化的閥值，使得二值化過後能精確的尋找到車牌。
車牌辨識系統的字元辨識，目前的方法有：類神經網路法、結構法、樣板比對法還有定點取樣法等。本論文比較過這些方法，就以類神經網路法用FPGA實做出來精準度比高且可以結構化設計，因為在FPGA上實現，可以讓系統同時比對10個數字和26個英文字母，以加快字元辨識這個過程。此系統在汽機車車牌定位成功率為98.18%，字元切割成功率為96.27%，辨識率為97.09%。

License plate recognition system has been widely researched and developed in the academic sphere and commercial area. The license plate recognition consists of three parts including license plate detection, character segmentation, and character recognition. License plate position uses image processing techniques which include grayscale, thresholding, mathematical morphology, and monnected-component labeling. These image processing have to accessed the neighboring pixels frequently to achieve intended mathematical formulas. To reduce the image data transfers, recently accessed pixels are stored in the register on FPGA. This leads to consume a large number of FPGA memory and logic resource in synthesizing our design, but image processing of proposed pipeline architecture on FPGA drastically decreases the amount of times of read and write to memory. The hardware based image processing system shows great speed up in the license plate recognition .
The proposed license plate detection applies Grayscale and Threshold. It uses important contrast features for plate object detection. Applying grayscale to the color input car image will filter the plate object. License plates in Taiwan are not only white background; they also can be red, green and yellow background. Except white, the other background colors of license plates are difficult to be located. The threshold method of this paper uses saturation, intensity and hue (HSI), to easily find attribute of the color plate and makes the plate location a precise task.
Character recognition of license plate recognition system uses neural network, syntactic structural, template matching and fixed-point sampling method. This paper applies neural network for character recognition. It is highly accurate and can be structurally designed on FPGA with great performance. Our experiment shows the proposed FPGA-based license plate recognition system achieves 98.18% license plate detection rate, 96.27% character segmentation rate, and 97.09% character recognition rate.

目錄
論文摘要 i
Abstract iii
致謝 v
目錄 vi
圖索引 viii
表索引 x
第一章 緒論 1
1-1研究動機與目的 1
1-2研究方向 1
1-3論文架構 2
第二章 相關知識 3
2-1色彩空間 3
2-1-1 RGB色彩空間 3
2-1-2 HSI色彩空間 4
2-2二值化 5
2-2-1固定門檻值 5
2-2-2像素平均門檻值 6
2-2-3統計式門檻值 6
2-3數學形態學 7
2-3-1膨脹 8
2-3-2侵蝕 9
2-3-3斷開與閉合 9
2-4連通物件法(Connected Component) 13
2-4-1四連通(4-neighbors) 14
2-4-2八連通(8-neighbors) 15
2-5字元辨識 (character recognition) 16
2-5-1字元正規化（Character Normalized） 16
2-5-2樣版比對法 16
2-6常見車牌辨識流程 17
2-7 SoPC技術 17
2-8 Avalon Bus 19
2-9 Altera NIOS II處理器 20
第三章 硬體架構 23
3-1車牌定位偵測模組 24
3-1-1 HSI模組 24
3-1-2二值化閥值 26
3-1-3侵蝕和膨脹模組 27
3-1-4車牌偵測前處理 28
3-1-5連通法模組 31
3-2字元定位偵測模組 33
3-3字元辨識模組 34
3-3-1雙線性內插法組 34
3-3-2類神經網路法 37
3-4 FPGA資源與記憶體頻寬分析 40
3-5系統建立與整合 41
第四章 系統測試與結果 43
4-1發展平台 43
4-2軟體設定 44
4-3系統測試與實做結果 46
4-4車牌辨識系統失敗探討 50
4-4-1車牌切割失敗 50
4-4-2字元切割失敗 50
4-4-3字元辨識失敗 51
第五章 結論與未來展望 52
5-1結論 52
5-2未來展望 52
參考文獻 53

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